Process Intelligence will transform banking: Here’s how

To meet these challenges and maintain a competitive advantage, institutions are optimizing their processes. Leaders of the world’s biggest banks regularly offer reassurance to shareholders that they remain focused on process transformation initiatives.

By focusing on process excellence, some of the biggest names in FSI have managed to keep pace with the rapidly changing banking landscape, while achieving outcomes like:

• 50% reduction in waiting time of credit approval
• 34% reduction in wait time for customers
• 100 days saved in credit time-to-recover
• 30% reduction in cycle time in cross-border payments

Pioneers such as Deutsche Bank have shown that optimizing the thousands of processes that come together to form banking operations leads to a step change in operating efficiency and a customer-experience boost that sets them apart from peers.

However, many banks still lack an understanding of the complexity and interconnectedness of these processes – and the importance of using AI-powered Process Intelligence to optimize them.

The process gaps holding back an industry

A white paper co-authored by Boston Consulting Group (BCG) and Celonis, BCG Banking (Ops) Excellence: How AI-driven Process Intelligence can transform banking operations, highlights that there’s often a disconnect between how a process is designed, how bank personnel think it operates, and how it runs in reality. These knowledge gaps tend to be compounded by a lack of shared understanding — particularly between front and back offices — on the role that processes play in shaping business outcomes.

The white paper sets out how the importance of efficient processes related to activities such as KYC (know your customer) and regulatory reporting are often underestimated as drivers of value. Meanwhile, business lines routinely operate in silos, and organizations as a whole often struggle to fully leverage their data. This results in too many banks missing out on value-creation and efficiency opportunities that could significantly boost their bottom and top lines.

Moving towards AI-powered Process Intelligence

Traditionally, the primary methods banks employ to understand how their processes work include process descriptions, targeted interviews, and value stream mapping (either physically or digitally).

But a more holistic approach — backed by Process Intelligence — can be a better way. With advanced AI applications in banking, banks can first leverage static historic data to create proofs of concept, and then run real-time diagnostics through the Celonis platform. This joined up methodology can reveal the links between processes — creating radical transparency on both operations processes and KPIs. New opportunities to unlock value can then be identified.

A three-stage transformation process

BCG and Celonis recommend that change programs are delivered in three distinct phases:

1. Assessment of potential value
2. Definition of a North Star, ideation and prioritization
3. Monitoring and steering

Phase 1: Assessment of potential value

This phase involves assessing the optimization potential of processes within a bank. In turn, this facilitates the effective prioritization of processes to optimize. Process Intelligence safely accesses data from almost any system — from enterprise resource planning (ERP) and customer relationship management (CRM) to custom-built platforms — and creates dynamic, real-time information flows.

The outcome is a baseline of knowledge that shines a new light on how the bank’s processes function. Teams are not only provided with KPIs but also recommendations and actionable insights. The fact that the approach is data-driven means it is relatively insulated from human bias. But selective stakeholder interviews and benchmarking can be added to test hypotheses and assumptions.

Phase 2: Define North Star, ideate and prioritize

The transparency offered by Process Intelligence empowers banks to define a North Star for initiatives like boosting operational efficiency, improving customer service or improving security. Digital process twins can be used to simulate process improvement measures and identify those with the highest impact, allowing for better decision-making. A range of innovation levers including natural language processing, GenAI and other AI use cases in banking can also be applied.

Phase 3: Monitoring & steering

According to the paper, the transition from strategy to implementation can be a challenge for many banks. Momentum generated in the strategy phase can rapidly dissipate, reducing potential business impacts. But Process Intelligence can bridge the gap, helping teams monitor process change adoption and make timely course corrections. A Process Intelligence “control tower” can act as both an aggregator of insights and a real-time lens on individual process steps.

New opportunities for AI in banking

Process Intelligence and AI are hugely complimentary technologies that can provide faster and clearer process insights and lead to smarter process optimization. As well as being informed by AI, Process Intelligence can also power AI models. Not least, Process Intelligence can feed large language models (LLMs) with correct, up-to-date data.

Our webinar on process intelligence and AI in banking explores this new frontier.

The paper outlines some incredible results with Process Intelligence projects. Its experience working with a range of financial institutions shows that potential performance uplifts include:

• Boosting process efficiency by up to 50%.
• Reducing time-to-yes and achieving 15% higher conversion rates.
• Lifting customer satisfaction scores by 10–15 points.
• Improving employee satisfaction, especially in repetitive and operational roles.
• Facilitating on-time submissions of regulatory reports, reducing the risk of fines.
• Sharpening decision-making through data-driven insights.
• Enabling monitoring of processes such as payments and trade settlements in real time.

At Celonis, we’ve seen many similar success stories in the sector. For example, one digital bank improved client conversion rates by about 50%, thanks to Process Intelligence. Meanwhile, a major European bank was able to reduce lead times in the retail process by 30% within six months. Credit applications were also processed four times faster.